ArticleFrontiers in oncology2021
A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Immunotherapy checkpoint inhibitor-combination therapy versus chemotherapy alone among Chinese population in cholangiocarcinoma treatment: a systematic review and meta-analysis.European journal of clinical pharmacology · 2026Pooled it
- Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.BMC cancer · 2023Trial
- Article
- Radiomics-Based Unsupervised Clustering Identifies Subtypes Associated With Prognosis and Immune Microenvironment in Clear Cell Renal Cell Carcinoma: A Multicenter Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data.Abdominal radiology (New York) · 2025Article
- Nomogram Development and Feature Selection Strategy Comparison for Predicting Surgical Site Infection After Lower Extremity Fracture Surgery.Medicina (Kaunas, Lithuania) · 2025Article
- Development and validation of a CT based radiomics nomogram for preoperative prediction of ISUP/WHO grading in renal clear cell carcinoma.Abdominal radiology (New York) · 2025Article
- Subregion-based radiomics analysis for predicting the histological grade of clear cell renal cell carcinoma.Frontiers in oncology · 2025Article
- Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study.Quantitative imaging in medicine and surgery · 2024Article
- Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.Abdominal radiology (New York) · 2024Article
- Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma.Frontiers in oncology · 2024Article
- A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.Cancer medicine · 2024Article
- Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions.Insights into imaging · 2023Article
- Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis.Frontiers in oncology · 2023Article
- Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.Frontiers in oncology · 2022Article
- The potential value of cuprotosis (copper-induced cell death) in the therapy of clear cell renal cell carcinoma.American journal of cancer research · 2022Article
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).
methods370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).
resultsUsing multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.
conclusionThe CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
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